Take-home Exercise 1

Detecting Spatial-Temporal Patterns of COVID-19 in Jakarta DKI.

Genice Goh true
09-10-2021

1.0 Introduction

This take-home exercise aims to reveal the spatial-temporal patterns of monthly cumulative confirmed COVID-19 rates and death rates in Jakarta DKI at the sub-district level.

2.0 The Data

For this assignment, the following data is used:

3.0 Instaling and Loading Packages

In this take-home exercise, the packages sf, tidyverse, tmap and readxl are used.

packages = c('sf', 'tidyverse', 'tmap', 'readxl')
for (p in packages){
  if(!require(p, character.only = T)){
    install.packages(p)
  }
  library(p,character.only = T)
}

4.0 Data Preparation and Wrangling

4.1 Geospatial Data

The geospatial data is imported with the use of st_read().

DKI <- st_read(dsn="data/geospatial",
               layer="BATAS_DESA_DESEMBER_2019_DUKCAPIL_DKI_JAKARTA")

Reading layer `BATAS_DESA_DESEMBER_2019_DUKCAPIL_DKI_JAKARTA' from data source `C:\Users\user\Desktop\SMU\Y4S1\IS415\geniceee\IS415_blog\_posts\2021-09-10-take-home-exercise-1\data\geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 269 features and 161 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 106.3831 ymin: -6.370815 xmax: 106.9728 ymax: -5.184322
Geodetic CRS:  WGS 84
glimpse(DKI)

Rows: 269
Columns: 162
$ OBJECT_ID  <dbl> 25477, 25478, 25397, 25400, 25378, 25379, 25390, ~
$ KODE_DESA  <chr> "3173031006", "3173031007", "3171031003", "317103~
$ DESA       <chr> "KEAGUNGAN", "GLODOK", "HARAPAN MULIA", "CEMPAKA ~
$ KODE       <dbl> 317303, 317303, 317103, 317103, 310101, 310101, 3~
$ PROVINSI   <chr> "DKI JAKARTA", "DKI JAKARTA", "DKI JAKARTA", "DKI~
$ KAB_KOTA   <chr> "JAKARTA BARAT", "JAKARTA BARAT", "JAKARTA PUSAT"~
$ KECAMATAN  <chr> "TAMAN SARI", "TAMAN SARI", "KEMAYORAN", "KEMAYOR~
$ DESA_KELUR <chr> "KEAGUNGAN", "GLODOK", "HARAPAN MULIA", "CEMPAKA ~
$ JUMLAH_PEN <dbl> 21609, 9069, 29085, 41913, 6947, 7059, 15793, 589~
$ JUMLAH_KK  <dbl> 7255, 3273, 9217, 13766, 2026, 2056, 5599, 1658, ~
$ LUAS_WILAY <dbl> 0.36, 0.37, 0.53, 0.97, 0.93, 0.95, 1.76, 1.14, 0~
$ KEPADATAN  <dbl> 60504, 24527, 54465, 42993, 7497, 7401, 8971, 515~
$ PERPINDAHA <dbl> 102, 25, 131, 170, 17, 26, 58, 13, 113, 178, 13, ~
$ JUMLAH_MEN <dbl> 68, 52, 104, 151, 14, 32, 36, 10, 60, 92, 5, 83, ~
$ PERUBAHAN  <dbl> 20464, 8724, 27497, 38323, 6853, 6993, 15006, 580~
$ WAJIB_KTP  <dbl> 16027, 7375, 20926, 30264, 4775, 4812, 12559, 398~
$ SILAM      <dbl> 15735, 1842, 26328, 36813, 6941, 7057, 7401, 5891~
$ KRISTEN    <dbl> 2042, 2041, 1710, 3392, 6, 0, 3696, 0, 4058, 5130~
$ KHATOLIK   <dbl> 927, 1460, 531, 1082, 0, 0, 1602, 0, 2100, 2575, ~
$ HINDU      <dbl> 15, 9, 42, 127, 0, 0, 622, 0, 25, 27, 0, 9, 115, ~
$ BUDHA      <dbl> 2888, 3716, 469, 495, 0, 2, 2462, 0, 4134, 4740, ~
$ KONGHUCU   <dbl> 2, 1, 5, 1, 0, 0, 10, 0, 9, 10, 0, 4, 1, 1, 4, 0,~
$ KEPERCAYAA <dbl> 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 2, 0, 22, 0, 3, ~
$ PRIA       <dbl> 11049, 4404, 14696, 21063, 3547, 3551, 7833, 2954~
$ WANITA     <dbl> 10560, 4665, 14389, 20850, 3400, 3508, 7960, 2937~
$ BELUM_KAWI <dbl> 10193, 4240, 14022, 20336, 3366, 3334, 7578, 2836~
$ KAWIN      <dbl> 10652, 4364, 13450, 19487, 3224, 3404, 7321, 2791~
$ CERAI_HIDU <dbl> 255, 136, 430, 523, 101, 80, 217, 44, 381, 476, 3~
$ CERAI_MATI <dbl> 509, 329, 1183, 1567, 256, 241, 677, 220, 1197, 9~
$ U0         <dbl> 1572, 438, 2232, 3092, 640, 648, 802, 585, 2220, ~
$ U5         <dbl> 1751, 545, 2515, 3657, 645, 684, 995, 588, 2687, ~
$ U10        <dbl> 1703, 524, 2461, 3501, 620, 630, 1016, 513, 2653,~
$ U15        <dbl> 1493, 521, 2318, 3486, 669, 671, 1106, 548, 2549,~
$ U20        <dbl> 1542, 543, 2113, 3098, 619, 609, 1081, 491, 2313,~
$ U25        <dbl> 1665, 628, 2170, 3024, 639, 582, 1002, 523, 2446,~
$ U30        <dbl> 1819, 691, 2363, 3188, 564, 592, 1236, 478, 2735,~
$ U35        <dbl> 1932, 782, 2595, 3662, 590, 572, 1422, 504, 3034,~
$ U40        <dbl> 1828, 675, 2371, 3507, 480, 486, 1200, 397, 2689,~
$ U45        <dbl> 1600, 607, 2250, 3391, 421, 457, 1163, 365, 2470,~
$ U50        <dbl> 1408, 619, 1779, 2696, 346, 369, 1099, 288, 2129,~
$ U55        <dbl> 1146, 602, 1379, 1909, 252, 318, 979, 235, 1843, ~
$ U60        <dbl> 836, 614, 1054, 1397, 197, 211, 880, 162, 1386, 1~
$ U65        <dbl> 587, 555, 654, 970, 122, 114, 747, 111, 958, 932,~
$ U70        <dbl> 312, 311, 411, 631, 69, 55, 488, 65, 554, 573, 38~
$ U75        <dbl> 415, 414, 420, 704, 74, 61, 577, 38, 717, 642, 37~
$ TIDAK_BELU <dbl> 3426, 1200, 4935, 7328, 1306, 1318, 2121, 973, 50~
$ BELUM_TAMA <dbl> 1964, 481, 2610, 3763, 730, 676, 1278, 732, 3241,~
$ TAMAT_SD   <dbl> 2265, 655, 2346, 2950, 1518, 2054, 1169, 1266, 44~
$ SLTP       <dbl> 3660, 1414, 3167, 5138, 906, 1357, 2236, 852, 585~
$ SLTA       <dbl> 8463, 3734, 12172, 16320, 2040, 1380, 5993, 1570,~
$ DIPLOMA_I  <dbl> 81, 23, 84, 179, 22, 15, 43, 36, 85, 83, 4, 63, 2~
$ DIPLOMA_II <dbl> 428, 273, 1121, 1718, 101, 59, 573, 97, 604, 740,~
$ DIPLOMA_IV <dbl> 1244, 1241, 2477, 4181, 314, 191, 2199, 357, 1582~
$ STRATA_II  <dbl> 74, 46, 166, 315, 10, 8, 168, 8, 63, 92, 5, 174, ~
$ STRATA_III <dbl> 4, 2, 7, 21, 0, 1, 13, 0, 3, 9, 0, 16, 8, 7, 75, ~
$ BELUM_TIDA <dbl> 3927, 1388, 5335, 8105, 1788, 1627, 2676, 1129, 5~
$ APARATUR_P <dbl> 81, 10, 513, 931, 246, 75, 156, 160, 132, 79, 23,~
$ TENAGA_PEN <dbl> 70, 43, 288, 402, 130, 93, 81, 123, 123, 73, 45, ~
$ WIRASWASTA <dbl> 8974, 3832, 10662, 14925, 788, 728, 6145, 819, 12~
$ PERTANIAN  <dbl> 1, 0, 1, 3, 2, 2, 1, 3, 2, 5, 1, 1, 0, 0, 2, 5, 2~
$ NELAYAN    <dbl> 0, 0, 2, 0, 960, 1126, 1, 761, 1, 2, 673, 0, 0, 0~
$ AGAMA_DAN  <dbl> 6, 6, 5, 40, 0, 0, 49, 2, 10, 11, 0, 54, 15, 16, ~
$ PELAJAR_MA <dbl> 4018, 1701, 6214, 9068, 1342, 1576, 3135, 1501, 6~
$ TENAGA_KES <dbl> 28, 29, 80, 142, 34, 26, 60, 11, 48, 55, 16, 68, ~
$ PENSIUNAN  <dbl> 57, 50, 276, 498, 20, 7, 59, 14, 56, 75, 2, 97, 5~
$ LAINNYA    <dbl> 4447, 2010, 5709, 7799, 1637, 1799, 3430, 1368, 7~
$ GENERATED  <chr> "30 Juni 2019", "30 Juni 2019", "30 Juni 2019", "~
$ KODE_DES_1 <chr> "3173031006", "3173031007", "3171031003", "317103~
$ BELUM_     <dbl> 3099, 1032, 4830, 7355, 1663, 1704, 2390, 1213, 5~
$ MENGUR_    <dbl> 4447, 2026, 5692, 7692, 1576, 1731, 3500, 1323, 7~
$ PELAJAR_   <dbl> 3254, 1506, 6429, 8957, 1476, 1469, 3185, 1223, 6~
$ PENSIUNA_1 <dbl> 80, 65, 322, 603, 24, 8, 70, 20, 75, 97, 2, 132, ~
$ PEGAWAI_   <dbl> 48, 5, 366, 612, 223, 72, 65, 143, 73, 48, 15, 89~
$ TENTARA    <dbl> 4, 0, 41, 57, 3, 0, 74, 1, 20, 12, 2, 11, 90, 340~
$ KEPOLISIAN <dbl> 10, 1, 16, 42, 11, 8, 2, 9, 17, 7, 3, 9, 165, 15,~
$ PERDAG_    <dbl> 31, 5, 1, 3, 6, 1, 2, 4, 3, 1, 4, 0, 1, 2, 9, 2, ~
$ PETANI     <dbl> 0, 0, 1, 2, 0, 1, 1, 0, 1, 1, 1, 2, 0, 0, 1, 2, 0~
$ PETERN_    <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ NELAYAN_1  <dbl> 1, 0, 1, 0, 914, 1071, 0, 794, 0, 1, 663, 0, 0, 0~
$ INDUSTR_   <dbl> 7, 3, 4, 3, 1, 3, 0, 0, 1, 7, 0, 0, 2, 2, 1, 3, 1~
$ KONSTR_    <dbl> 3, 0, 2, 6, 3, 8, 1, 6, 1, 5, 10, 0, 2, 5, 7, 4, ~
$ TRANSP_    <dbl> 2, 0, 7, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 6, 3, 2~
$ KARYAW_    <dbl> 6735, 3034, 7347, 10185, 237, 264, 4319, 184, 940~
$ KARYAW1    <dbl> 9, 2, 74, 231, 4, 0, 16, 1, 13, 10, 1, 24, 17, 29~
$ KARYAW1_1  <dbl> 0, 0, 5, 15, 0, 0, 0, 1, 0, 1, 0, 0, 2, 4, 7, 9, ~
$ KARYAW1_12 <dbl> 23, 4, 25, 35, 141, 50, 16, 157, 6, 9, 40, 11, 11~
$ BURUH      <dbl> 515, 155, 971, 636, 63, 218, 265, 55, 1085, 652, ~
$ BURUH_     <dbl> 1, 0, 0, 0, 2, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 2, 1~
$ BURUH1     <dbl> 0, 0, 1, 0, 1, 25, 0, 2, 0, 1, 1, 0, 0, 0, 0, 0, ~
$ BURUH1_1   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ PEMBANT_   <dbl> 1, 1, 4, 1, 1, 0, 7, 0, 5, 1, 0, 6, 1, 10, 11, 9,~
$ TUKANG     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0~
$ TUKANG_1   <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ TUKANG_12  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ TUKANG__13 <dbl> 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0~
$ TUKANG__14 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ TUKANG__15 <dbl> 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0~
$ TUKANG__16 <dbl> 7, 4, 10, 14, 0, 0, 2, 0, 7, 8, 0, 8, 1, 0, 3, 2,~
$ TUKANG__17 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0~
$ PENATA     <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0~
$ PENATA_    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ PENATA1_1  <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 2, 0, 1, 0, 0, 0~
$ MEKANIK    <dbl> 11, 1, 10, 8, 0, 0, 4, 0, 7, 8, 0, 9, 0, 15, 10, ~
$ SENIMAN_   <dbl> 4, 0, 12, 28, 0, 0, 2, 0, 3, 4, 0, 9, 6, 7, 14, 1~
$ TABIB      <dbl> 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0~
$ PARAJI_    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ PERANCA_   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 1, 0, 1~
$ PENTER_    <dbl> 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2~
$ IMAM_M     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ PENDETA    <dbl> 2, 4, 5, 33, 0, 0, 20, 0, 10, 8, 0, 30, 14, 14, 1~
$ PASTOR     <dbl> 0, 1, 0, 1, 0, 0, 8, 0, 0, 0, 0, 23, 0, 0, 0, 0, ~
$ WARTAWAN   <dbl> 7, 1, 16, 27, 0, 0, 4, 0, 8, 6, 0, 9, 5, 9, 26, 3~
$ USTADZ     <dbl> 6, 1, 1, 5, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0~
$ JURU_M     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0~
$ PROMOT     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ ANGGOTA_   <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 2, 1, 2~
$ ANGGOTA1   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0~
$ ANGGOTA1_1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ PRESIDEN   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ WAKIL_PRES <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1~
$ ANGGOTA1_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0~
$ ANGGOTA1_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0~
$ DUTA_B     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1~
$ GUBERNUR   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1~
$ WAKIL_GUBE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ BUPATI     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ WAKIL_BUPA <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ WALIKOTA   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ WAKIL_WALI <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ ANGGOTA1_4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1~
$ ANGGOTA1_5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ DOSEN      <dbl> 3, 2, 23, 36, 1, 2, 11, 0, 3, 5, 0, 14, 6, 28, 69~
$ GURU       <dbl> 72, 40, 272, 378, 118, 72, 69, 116, 126, 71, 36, ~
$ PILOT      <dbl> 1, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 1~
$ PENGACARA_ <dbl> 4, 1, 8, 22, 0, 0, 5, 0, 5, 4, 0, 4, 3, 12, 24, 2~
$ NOTARIS    <dbl> 0, 0, 3, 5, 0, 0, 4, 0, 0, 0, 0, 5, 0, 5, 10, 3, ~
$ ARSITEK    <dbl> 1, 0, 2, 3, 0, 0, 2, 0, 0, 0, 0, 4, 1, 2, 7, 3, 9~
$ AKUNTA_    <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 2~
$ KONSUL_    <dbl> 1, 0, 2, 11, 0, 0, 4, 0, 0, 0, 0, 6, 2, 3, 10, 8,~
$ DOKTER     <dbl> 16, 32, 35, 68, 0, 1, 63, 0, 27, 32, 1, 63, 48, 6~
$ BIDAN      <dbl> 3, 1, 9, 18, 12, 8, 1, 3, 3, 3, 7, 3, 10, 10, 7, ~
$ PERAWAT    <dbl> 7, 0, 25, 44, 12, 10, 3, 6, 12, 20, 6, 7, 26, 16,~
$ APOTEK_    <dbl> 0, 0, 2, 3, 1, 0, 0, 0, 1, 2, 0, 1, 2, 3, 3, 3, 3~
$ PSIKIATER  <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 2, 1~
$ PENYIA_    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0~
$ PENYIA1    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ PELAUT     <dbl> 0, 0, 6, 16, 1, 1, 0, 14, 2, 4, 1, 2, 4, 2, 10, 1~
$ PENELITI   <dbl> 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1~
$ SOPIR      <dbl> 65, 3, 94, 123, 0, 1, 61, 0, 76, 79, 0, 63, 44, 1~
$ PIALAN     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ PARANORMAL <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0~
$ PEDAGA_    <dbl> 379, 126, 321, 562, 11, 10, 412, 15, 202, 225, 0,~
$ PERANG_    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ KEPALA_    <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ BIARAW_    <dbl> 0, 1, 0, 0, 0, 0, 22, 0, 3, 0, 0, 2, 1, 0, 4, 0, ~
$ WIRASWAST_ <dbl> 1370, 611, 1723, 3099, 131, 119, 1128, 259, 2321,~
$ LAINNYA_12 <dbl> 94, 57, 82, 122, 12, 10, 41, 6, 89, 158, 24, 37, ~
$ LUAS_DESA  <dbl> 25476, 25477, 25396, 25399, 25377, 25378, 25389, ~
$ KODE_DES_3 <chr> "3173031006", "3173031007", "3171031003", "317103~
$ DESA_KEL_1 <chr> "KEAGUNGAN", "GLODOK", "HARAPAN MULIA", "CEMPAKA ~
$ KODE_12    <dbl> 317303, 317303, 317103, 317103, 310101, 310101, 3~
$ geometry   <MULTIPOLYGON [°]> MULTIPOLYGON (((106.8164 -6..., MULT~

4.1.1 Checking for NA values

Since the value TRUE is returned, it implies that the dataset contains NA values.

sum(is.na(DKI)) > 0

[1] TRUE

The function drop_na is used to drop the NA values in the dataset.

DKI <- drop_na(DKI)

4.1.2 CRS Check and Reprojection

The st_crs() function is used to check the coordinate reference system of the dataset.

st_crs(DKI)

Coordinate Reference System:
  User input: WGS 84 
  wkt:
GEOGCRS["WGS 84",
    DATUM["World Geodetic System 1984",
        ELLIPSOID["WGS 84",6378137,298.257223563,
            LENGTHUNIT["metre",1]]],
    PRIMEM["Greenwich",0,
        ANGLEUNIT["degree",0.0174532925199433]],
    CS[ellipsoidal,2],
        AXIS["latitude",north,
            ORDER[1],
            ANGLEUNIT["degree",0.0174532925199433]],
        AXIS["longitude",east,
            ORDER[2],
            ANGLEUNIT["degree",0.0174532925199433]],
    ID["EPSG",4326]]

Since the dataset is in WGS84 projection system, there is a need to transform it to the national projected coordinates system of Indonesia (DGN95/Indonesia TM-3 zone 54.1), which has an EPSG code of 23845.

DKI23845 <- st_transform(DKI, crs = 23845)
st_crs(DKI23845)

Coordinate Reference System:
  User input: EPSG:23845 
  wkt:
PROJCRS["DGN95 / Indonesia TM-3 zone 54.1",
    BASEGEOGCRS["DGN95",
        DATUM["Datum Geodesi Nasional 1995",
            ELLIPSOID["WGS 84",6378137,298.257223563,
                LENGTHUNIT["metre",1]]],
        PRIMEM["Greenwich",0,
            ANGLEUNIT["degree",0.0174532925199433]],
        ID["EPSG",4755]],
    CONVERSION["Indonesia TM-3 zone 54.1",
        METHOD["Transverse Mercator",
            ID["EPSG",9807]],
        PARAMETER["Latitude of natural origin",0,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8801]],
        PARAMETER["Longitude of natural origin",139.5,
            ANGLEUNIT["degree",0.0174532925199433],
            ID["EPSG",8802]],
        PARAMETER["Scale factor at natural origin",0.9999,
            SCALEUNIT["unity",1],
            ID["EPSG",8805]],
        PARAMETER["False easting",200000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8806]],
        PARAMETER["False northing",1500000,
            LENGTHUNIT["metre",1],
            ID["EPSG",8807]]],
    CS[Cartesian,2],
        AXIS["easting (X)",east,
            ORDER[1],
            LENGTHUNIT["metre",1]],
        AXIS["northing (Y)",north,
            ORDER[2],
            LENGTHUNIT["metre",1]],
    USAGE[
        SCOPE["Cadastre."],
        AREA["Indonesia - onshore east of 138°E."],
        BBOX[-9.19,138,-1.49,141.01]],
    ID["EPSG",23845]]

4.1.3 Exclude Outer Islands

The tmap() function is used to plot the geospatial data for easy visualisation.

tmap_mode("view")
tm_shape(DKI23845) + 
  tm_polygons()

While looking at the data points of the outer islands using the interactive map, it can be observed that they share the same value “KEPULAUAN SERIBU” for the field “KAB_KOTA”. This information can therefore be used to exclude the outer islands from the dataframe.

tmap_mode('plot')

DKI23845 <- DKI23845 %>% 
  filter(`KAB_KOTA` != "KEPULAUAN SERIBU")

plot(DKI23845)

4.1.4 Select First 9 Fields

DKI23845 <- DKI23845 %>% select(0:9)

DKI23845

Simple feature collection with 261 features and 9 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: -3644275 ymin: 663887.8 xmax: -3606237 ymax: 701380.1
Projected CRS: DGN95 / Indonesia TM-3 zone 54.1
First 10 features:
   OBJECT_ID  KODE_DESA               DESA   KODE    PROVINSI
1      25477 3173031006          KEAGUNGAN 317303 DKI JAKARTA
2      25478 3173031007             GLODOK 317303 DKI JAKARTA
3      25397 3171031003      HARAPAN MULIA 317103 DKI JAKARTA
4      25400 3171031006       CEMPAKA BARU 317103 DKI JAKARTA
5      25390 3171021001         PASAR BARU 317102 DKI JAKARTA
6      25391 3171021002       KARANG ANYAR 317102 DKI JAKARTA
7      25394 3171021005 MANGGA DUA SELATAN 317102 DKI JAKARTA
8      25386 3171011003       PETOJO UTARA 317101 DKI JAKARTA
9      25403 3171041001              SENEN 317104 DKI JAKARTA
10     25408 3171041006             BUNGUR 317104 DKI JAKARTA
        KAB_KOTA   KECAMATAN         DESA_KELUR JUMLAH_PEN
1  JAKARTA BARAT  TAMAN SARI          KEAGUNGAN      21609
2  JAKARTA BARAT  TAMAN SARI             GLODOK       9069
3  JAKARTA PUSAT   KEMAYORAN      HARAPAN MULIA      29085
4  JAKARTA PUSAT   KEMAYORAN       CEMPAKA BARU      41913
5  JAKARTA PUSAT SAWAH BESAR         PASAR BARU      15793
6  JAKARTA PUSAT SAWAH BESAR       KARANG ANYAR      33383
7  JAKARTA PUSAT SAWAH BESAR MANGGA DUA SELATAN      35906
8  JAKARTA PUSAT      GAMBIR       PETOJO UTARA      21828
9  JAKARTA PUSAT       SENEN              SENEN       8643
10 JAKARTA PUSAT       SENEN             BUNGUR      23001
                         geometry
1  MULTIPOLYGON (((-3626874 69...
2  MULTIPOLYGON (((-3627130 69...
3  MULTIPOLYGON (((-3621251 68...
4  MULTIPOLYGON (((-3620608 69...
5  MULTIPOLYGON (((-3624097 69...
6  MULTIPOLYGON (((-3624785 69...
7  MULTIPOLYGON (((-3624752 69...
8  MULTIPOLYGON (((-3626121 69...
9  MULTIPOLYGON (((-3623189 69...
10 MULTIPOLYGON (((-3622451 69...

4.2 Aspatial Data

The aspatial data is imported using the read_excel function. Since data is only required at the sub-district level, data will only be extracted from the “data” sheet.

mar20 <- read_excel("data/aspatial/Standar Kelurahan Data Corona (31 Maret 2020 Pukul 08.00).xlsx", sheet="data")

apr20 <- read_excel("data/aspatial/Standar Kelurahan Data Corona (30 April 2020 Pukul 09.00).xlsx", sheet="data") 

may20 <- read_excel("data/aspatial/Standar Kelurahan Data Corona (31 MEI 2020 Pukul 09.00).xlsx", sheet="data") 

jun20 <- read_excel("data/aspatial/Standar Kelurahan Data Corona (30 Juni 2020 Pukul 09.00).xlsx", sheet="data") 

jul20 <- read_excel("data/aspatial/Standar Kelurahan Data Corona (31 Juli 2020 Pukul 09.00).xlsx", sheet="data") 

aug20 <- read_excel("data/aspatial/Standar Kelurahan Data Corona (31 Agustus 2020 Pukul 10.00).xlsx", sheet="data") 

sep20 <- read_excel("data/aspatial/Standar_Kelurahan_Data_Corona_30_September_2020_Pukul_10_00.xlsx", sheet="data") 

oct20 <- read_excel("data/aspatial/Standar Kelurahan Data Corona (31 Oktober 2020 Pukul 10.00).xlsx", sheet="data") 

nov20 <- read_excel("data/aspatial/Standar_Kelurahan_Data_Corona_30_November_2020_Pukul_10_00.xlsx", sheet="data") 

dec20 <- read_excel("data/aspatial/Standar_Kelurahan_Data_Corona_26_Desember_2020_Pukul_10_00.xlsx", sheet="data") 

jan21 <- read_excel("data/aspatial/Standar Kelurahan Data Corona (30 Januari 2021 Pukul 10.00).xlsx", sheet="data") 

feb21 <- read_excel("data/aspatial/Standar_Kelurahan_Data_Corona_27_Februari_2021_Pukul_10_00.xlsx", sheet="data")

mar21 <- read_excel("data/aspatial/Standar Kelurahan Data Corona (27 Maret 2021 Pukul 10.00).xlsx", sheet="data") 

apr21 <- read_excel("data/aspatial/Standar Kelurahan Data Corona (24 April 2021 Pukul 10.00).xlsx", sheet="data")

may21 <- read_excel("data/aspatial/Standar Kelurahan Data Corona (29 Mei 2021 Pukul 10.00).xlsx", sheet="data") 

jun21 <- read_excel("data/aspatial/Standar Kelurahan Data Corona (26 Juni 2021 Pukul 10.00).xlsx", sheet="data") 

jul21 <- read_excel("data/aspatial/Standar Kelurahan Data Corona (31 Juli 2021 Pukul 10.00).xlsx", sheet="data")

4.2.1 Extracting Required Columns

In the case of duplicated columns, values from the 2nd column are retained for consistency. There is also a need to create a new column called Month, with values following the respective months.

mar20_filtered <- mar20 %>% 
  select("ID_KEL"="ID_KEL...2", "Nama_provinsi", "nama_kota", 
         "nama_kecamatan", "nama_kelurahan", "POSITIF", "Meninggal") %>%
  mutate("Month"= "Mar20")

apr20_filtered <- apr20 %>% 
  select("ID_KEL"="ID_KEL...2", "Nama_provinsi", "nama_kota", 
         "nama_kecamatan", "nama_kelurahan", "POSITIF", "Meninggal") %>%
  mutate("Month"= "Apr20")

may20_filtered <- may20 %>% 
  select("ID_KEL"="ID_KEL...2", "Nama_provinsi", "nama_kota", 
         "nama_kecamatan", "nama_kelurahan", "POSITIF", "Meninggal") %>%
  mutate("Month"= "May20")

jun20_filtered <- jun20 %>% 
  select("ID_KEL"="ID_KEL...2", "Nama_provinsi", "nama_kota", 
         "nama_kecamatan", "nama_kelurahan", "POSITIF", "Meninggal") %>%
  mutate("Month"= "Jun20")

jul20_filtered <- jul20 %>% 
  select("ID_KEL", "Nama_provinsi", "nama_kota", "nama_kecamatan", 
         "nama_kelurahan", "POSITIF", "Meninggal"="Meninggal...26") %>%
  mutate("Month"= "Jul20")

aug20_filtered <- aug20 %>% 
  select("ID_KEL", "Nama_provinsi", "nama_kota", "nama_kecamatan", 
         "nama_kelurahan", "POSITIF", "Meninggal"="Meninggal...28") %>%
  mutate("Month"= "Aug20")

sep20_filtered <- sep20 %>% 
  select("ID_KEL", "Nama_provinsi", "nama_kota", "nama_kecamatan", 
         "nama_kelurahan", "POSITIF", "Meninggal"="Meninggal...29") %>%
  mutate("Month"= "Sep20")

oct20_filtered <- oct20 %>% 
  select("ID_KEL", "Nama_provinsi", "nama_kota", "nama_kecamatan", 
         "nama_kelurahan", "POSITIF", "Meninggal"="Meninggal...30") %>%
  mutate("Month"= "Oct20")

nov20_filtered <- nov20 %>% 
  select("ID_KEL", "Nama_provinsi", "nama_kota", "nama_kecamatan", 
         "nama_kelurahan", "POSITIF", "Meninggal"="Meninggal...30") %>%
  mutate("Month"= "Nov20")

dec20_filtered <- dec20 %>% 
  select("ID_KEL", "Nama_provinsi", "nama_kota", "nama_kecamatan", 
         "nama_kelurahan", "POSITIF", "Meninggal"="Meninggal...30") %>%
  mutate("Month"= "Dec20")

jan21_filtered <- jan21 %>% 
  select("ID_KEL", "Nama_provinsi", "nama_kota", "nama_kecamatan", 
         "nama_kelurahan", "POSITIF", "Meninggal"="Meninggal...31") %>%
  mutate("Month"= "Jan21")

feb21_filtered <- feb21 %>% 
  select("ID_KEL", "Nama_provinsi", "nama_kota", "nama_kecamatan", 
         "nama_kelurahan", "POSITIF", "Meninggal"="Meninggal...31") %>%
  mutate("Month"= "Feb21")

mar21_filtered <- mar21 %>% 
  select("ID_KEL", "Nama_provinsi", "nama_kota", "nama_kecamatan",
         "nama_kelurahan", "POSITIF", "Meninggal"="Meninggal...31") %>%
  mutate("Month"= "Mar21")

apr21_filtered <- apr21 %>% 
  select("ID_KEL", "Nama_provinsi", "nama_kota", "nama_kecamatan",
         "nama_kelurahan", "POSITIF", "Meninggal"="Meninggal...31") %>%
  mutate("Month"= "Apr21")

may21_filtered <- may21 %>% 
  select("ID_KEL", "Nama_provinsi", "nama_kota", "nama_kecamatan",
         "nama_kelurahan", "POSITIF", "Meninggal"="Meninggal...31") %>%
  mutate("Month"= "May21")

jun21_filtered <- jun21 %>% 
  select("ID_KEL", "Nama_provinsi", "nama_kota", "nama_kecamatan",
         "nama_kelurahan", "POSITIF", "Meninggal"="Meninggal...31") %>%
  mutate("Month"= "Jun21")

jul21_filtered <- jul21 %>% 
  select("ID_KEL", "Nama_provinsi", "nama_kota", "nama_kecamatan",
         "nama_kelurahan", "POSITIF", "Meninggal"="Meninggal...31") %>%
  mutate("Month"= "Jul21")

4.2.2 Integrating Monthly Data

covidDKI <- bind_rows(mar20_filtered, apr20_filtered, may20_filtered,
                      jun20_filtered, jul20_filtered, aug20_filtered,
                      sep20_filtered, oct20_filtered, nov20_filtered,
                      dec20_filtered, jan21_filtered, feb21_filtered,
                      mar21_filtered, apr21_filtered, may21_filtered,
                      jun21_filtered, jul21_filtered)

covidDKI

4.2.3 Checking for NA values

Since the value TRUE is returned, it implies that the dataset contains NA values.

sum(is.na(covidDKI)) > 0

The function drop_na is used to drop the NA values in the dataset.

covidDKI <- drop_na(covidDKI)

4.2.4 Data Cleaning

While looking through the cleaned dataset, it can be observed there are rows where the ID_KEL column has non-integer values. These observations are therefore removed.

covidDKI <- covidDKI %>%
  filter(`ID_KEL` != "LUAR DKI JAKARTA") %>% 
  filter(`ID_KEL` != "PROSES UPDATE DATA") %>% 
  filter(`ID_KEL` != "BELUM DIKETAHUI")

Moreover, several data points in the column “nama_kelurahan” has values in both the short and long form. There is therefore a need to convert those values in the short form to the long form to aid later data wrangling.

covidDKI$nama_kelurahan <- gsub("P\\.", "PULAU", covidDKI$nama_kelurahan)

4.2.5 Save Aspatial Data as RDS

covidDKI_rds <- write_rds(covidDKI, "data/rds/covidDKI.rds")

The RDS aspatial data is now used to proceed with the later analysis.

covidDKI <- read_rds("data/rds/covidDKI.rds")

4.2.6 Prepare Cumulative Monthly Data in Columns

Since it is required later to plot the cumulative confirmed cases rates and death rates by month, the dataset is pivoted to a wide version.

covidDKI <- covidDKI %>%
  group_by(ID_KEL, Nama_provinsi, nama_kota, nama_kecamatan, 
           nama_kelurahan, Month) %>%
  summarise(`MENINGGAL` = sum(`Meninggal`), `POSITIF` = sum(`POSITIF`)) %>%
  ungroup() %>%
  pivot_wider(names_from=Month, values_from=c(POSITIF, MENINGGAL))

4.3 Combine Aspatial and Geospatial Data

The function right_join() is used to combine the aspatial and geospatial data.

jakartaDKI <- right_join(covidDKI, DKI23845,
                         by= c("ID_KEL" = "KODE_DESA"))

4.3.1 Checking for NA values

Since the value returned is FALSE, there are no NA vlues in the dataset.

sum(is.na(jakartaDKI)) > 0

[1] FALSE

4.4 Cumulative Confirmed Cases and Death Rates by Month

There is a need to generate the total number of confirmed cases and deaths in each area using rowSums() and generate 2 new columns based on these values using mutate().

jakartaDKI <- jakartaDKI %>%
  mutate(`TOTAL POSITIF` = rowSums(.[6:22])) %>%
  mutate(`TOTAL MENINGGAL` = rowSums(.[23:39]))

The cumulative confirmed cases and death rates (per 10000 population) by month is then calculated.

jakartaDKI_rates <- jakartaDKI 

jakartaDKI_rates[6:22] <- 
  (jakartaDKI_rates[6:22] / jakartaDKI_rates$JUMLAH_PEN) * 10000

jakartaDKI_rates[23:39] <-
  (jakartaDKI_rates[23:39] / jakartaDKI_rates$JUMLAH_PEN) * 10000

4.5 Calculate Relative Risk

Relative risk can be calculated as shown:

jakartaDKI_risk <- jakartaDKI %>%
  mutate(`RELATIVE_RISK` = (rowSums(.[23:39]) * 100) / 
           (rowSums(.[23:39]) * jakartaDKI$JUMLAH_PEN))

5.0 Thematic Mapping

Choropleth mapping techniques will be used to analyse the spatial-temporal patterns of monthly cumulative confirmed cases rates and death rates in Jakarta DKI at the sub-district level.

5.1 Converting Datasets to sf format

The required datasets need to be converted to sf format using st_as_sf() so that we can create plots using tmap.

jakartaDKI <- st_as_sf(jakartaDKI)
jakartaDKI_rates <- st_as_sf(jakartaDKI_rates)
jakartaDKI_risk <- st_as_sf(jakartaDKI_risk)

5.2 Classification Method

In order to have a common basis of comparison among the choropleth maps, they need to use the same classification method.

The equal intervals classification method with custom breaks will be utilised to plot the choropleth map.

5.2.1 Selecting Custom Breaks

To specify the intervals of the break points, descriptive statistics of the cumulative confirmed cases rates and deaths rates are examined.

summary(jakartaDKI_rates$POSITIF_Mar20)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.3264  0.7609  0.6928 49.8826 
summary(jakartaDKI_rates$POSITIF_Jul21)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  187.3   545.1   658.3   705.3   779.7  3808.3 
summary(jakartaDKI_rates$MENINGGAL_Mar20)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.00000 0.08094 0.00000 3.05344 
summary(jakartaDKI_rates$MENINGGAL_Jul21)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  3.439   8.446  10.298  10.602  12.508  42.098

For both cumulative confirmed cases rates and death rates, it should be kept in mind that there may be over-generalisation of map data due to the intervals of the break points. This is especially during the earlier months where the cumulative confirmed cases rates and death rates are both low, and important spatial trends may have been missed.

As a result, the Jenks Classification Method will be utilised to plot the choropleth maps as well. This is because it allows for natural breaks, where break points are specified in a way that maximises within-group homogeneity and between-group heterogeneity. This can help us to capture potential critical spatial trends that we may have overlooked previously.

5.3 Equal Intervals Classification Method

The equal_positif and equal_meninggal functions are created to perform choropleth mapping using the equal intervals classification method with custom breaks.

As mentioned earlier, there are a total number of 8 classes defined, with an interval of 476 for the cumulative confirmed cases rates.

equal_positif <- function(df, vnam) {
  tm_shape(jakartaDKI_rates)+
  tm_fill(vnam,
          breaks = c(0, 476, 952, 1428, 1904, 2380, 2856, 3332, 3808),
          palette="Blues") +
  tm_borders(lwd = 0.1,
             alpha = 0.3) + 
  tm_layout(panel.show = TRUE,
            panel.labels = vnam,
            panel.label.size = 0.5,
            legend.show=FALSE)
}

For the cumulative death rates, there are a total number of 7 classes defined, with an interval of 6.

equal_meninggal <- function(df, vnam) {
  tm_shape(jakartaDKI_rates)+
  tm_fill(vnam,
          breaks = c(0, 6, 12, 18, 24, 30, 36, 42),
          palette="Blues") +
  tm_borders(lwd = 0.1,
             alpha = 0.3) + 
  tm_layout(panel.show = TRUE,
            panel.labels = vnam,
            panel.label.size = 0.5,
            legend.show=FALSE)
}

As seen in the code chunks below, the equal_positif and equal_meninggal functions can be easily applied to create choropleth maps for the cumulative confirmed cases rates and death rates without much duplicate code. The choropleth maps are then arranged together using tmap_arrange() to aid easier spotting of patterns over space and time.

5.3.1 Cumulative Confirmed Cases Rates By Month

tmap_arrange(equal_positif(jakartaDKI_rates, "POSITIF_Mar20"), 
             equal_positif(jakartaDKI_rates, "POSITIF_Apr20"),
             equal_positif(jakartaDKI_rates, "POSITIF_May20"),
             equal_positif(jakartaDKI_rates, "POSITIF_Jun20"),
             equal_positif(jakartaDKI_rates, "POSITIF_Jul20"),
             equal_positif(jakartaDKI_rates, "POSITIF_Aug20"),
             equal_positif(jakartaDKI_rates, "POSITIF_Sep20"), 
             equal_positif(jakartaDKI_rates, "POSITIF_Oct20"), 
             equal_positif(jakartaDKI_rates, "POSITIF_Nov20"), 
             equal_positif(jakartaDKI_rates, "POSITIF_Dec20"), 
             equal_positif(jakartaDKI_rates, "POSITIF_Jan21"), 
             equal_positif(jakartaDKI_rates, "POSITIF_Feb21"), 
             equal_positif(jakartaDKI_rates, "POSITIF_Mar21"), 
             equal_positif(jakartaDKI_rates, "POSITIF_Apr21"), 
             equal_positif(jakartaDKI_rates, "POSITIF_May21"), 
             equal_positif(jakartaDKI_rates, "POSITIF_Jun21"), 
             equal_positif(jakartaDKI_rates, "POSITIF_Jul21"))

5.3.2 Cumulative Death Rates By Month

tmap_arrange(equal_meninggal(jakartaDKI_rates, "MENINGGAL_Mar20"), 
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_Apr20"),
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_May20"),
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_Jun20"),
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_Jul20"),
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_Aug20"),
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_Sep20"), 
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_Oct20"), 
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_Nov20"), 
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_Dec20"), 
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_Jan21"), 
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_Feb21"), 
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_Mar21"), 
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_Apr21"), 
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_May21"), 
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_Jun21"), 
             equal_meninggal(jakartaDKI_rates, "MENINGGAL_Jul21"))

Similar to what we have predicted before, we cannot identify significant spatial patterns from March 2020 to January 2021, which may have been lost due to the break points defined. As a result, choropleth maps using the Jenks classification method will be created to give further insights.

5.4 Jenks Classification Method

The jenks function is created to perform choropleth mapping using the Jenks classification method.

jenks <- function(df, vnam){
  tm_shape(df) +
     tm_fill(vnam,
             n = 5,
             style = "jenks",
             palette="Blues")  +
  tm_borders(lwd = 0.1,
             alpha = 0.3) +
  tm_layout(panel.show = TRUE,
            panel.labels = vnam,
            panel.label.size = 0.5,
            legend.show=FALSE)
}

As seen in the code chunks below, the jenks function can be easily applied to create choropleth maps for the cumulative confirmed cases rates and death rates without much duplicate code. The choropleth maps are then arranged together using tmap_arrange() to aid easier spotting of patterns over space and time.

5.4.1 Cumulative Confirmed Cases Rates By Month

tmap_arrange(jenks(jakartaDKI_rates, "POSITIF_Mar20"), 
             jenks(jakartaDKI_rates, "POSITIF_Apr20"),
             jenks(jakartaDKI_rates, "POSITIF_May20"),
             jenks(jakartaDKI_rates, "POSITIF_Jun20"),
             jenks(jakartaDKI_rates, "POSITIF_Jul20"),
             jenks(jakartaDKI_rates, "POSITIF_Aug20"),
             jenks(jakartaDKI_rates, "POSITIF_Sep20"), 
             jenks(jakartaDKI_rates, "POSITIF_Oct20"), 
             jenks(jakartaDKI_rates, "POSITIF_Nov20"), 
             jenks(jakartaDKI_rates, "POSITIF_Dec20"), 
             jenks(jakartaDKI_rates, "POSITIF_Jan21"), 
             jenks(jakartaDKI_rates, "POSITIF_Feb21"), 
             jenks(jakartaDKI_rates, "POSITIF_Mar21"), 
             jenks(jakartaDKI_rates, "POSITIF_Apr21"), 
             jenks(jakartaDKI_rates, "POSITIF_May21"), 
             jenks(jakartaDKI_rates, "POSITIF_Jun21"), 
             jenks(jakartaDKI_rates, "POSITIF_Jul21"))

5.4.2 Cumulative Death Rates By Month

tmap_arrange(jenks(jakartaDKI_rates, "MENINGGAL_Mar20"), 
             jenks(jakartaDKI_rates, "MENINGGAL_Apr20"),
             jenks(jakartaDKI_rates, "MENINGGAL_May20"),
             jenks(jakartaDKI_rates, "MENINGGAL_Jun20"),
             jenks(jakartaDKI_rates, "MENINGGAL_Jul20"),
             jenks(jakartaDKI_rates, "MENINGGAL_Aug20"),
             jenks(jakartaDKI_rates, "MENINGGAL_Sep20"), 
             jenks(jakartaDKI_rates, "MENINGGAL_Oct20"), 
             jenks(jakartaDKI_rates, "MENINGGAL_Nov20"), 
             jenks(jakartaDKI_rates, "MENINGGAL_Dec20"), 
             jenks(jakartaDKI_rates, "MENINGGAL_Jan21"), 
             jenks(jakartaDKI_rates, "MENINGGAL_Feb21"), 
             jenks(jakartaDKI_rates, "MENINGGAL_Mar21"), 
             jenks(jakartaDKI_rates, "MENINGGAL_Apr21"), 
             jenks(jakartaDKI_rates, "MENINGGAL_May21"), 
             jenks(jakartaDKI_rates, "MENINGGAL_Jun21"), 
             jenks(jakartaDKI_rates, "MENINGGAL_Jul21"))

6.0 Analytical Mapping

Extreme value maps are variations of common choropleth maps where the classification is designed to highlight extreme values at the lower and upper end of the scale, with the goal of identifying outliers.

6.1 Box Maps

A box map displays summary statistics on a choropleth map by using the basic principles of box plots. Since they are based on the principles of box plots, they can be used to detect outliers in a stricter sense than is possible with percentile maps.

The code below shows the boxbreak function, which is used to create break points for a box map.

 boxbreaks <- function(v,mult=1.5) {
   qv <- unname(quantile(v))
   iqr <- qv[4] - qv[2]
   upfence <- qv[4] + mult * iqr
   lofence <- qv[2] - mult * iqr
   # initialize break points vector
   bb <- vector(mode="numeric",length=7)
   # logic for lower and upper fences
   if (lofence < qv[1]) {  # no lower outliers
     bb[1] <- lofence
     bb[2] <- floor(qv[1])
   } else {
     bb[2] <- lofence
     bb[1] <- qv[1]
   }
   if (upfence > qv[5]) { # no upper outliers
     bb[7] <- upfence
     bb[6] <- ceiling(qv[5])
   } else {
     bb[6] <- upfence
     bb[7] <- qv[5]
   }
   bb[3:5] <- qv[2:4]
   return(bb)
}

The code chunk below shows the get.var function, which extracts a variable as a vector out of an sf data frame.

get.var <- function(vname, df) {
  v <- df[vname] 
  v <- unname(v[[1]])
  return(v)
}

Lastly, the code chunk below shows the boxmap function, which helps to create a box map.

 boxmap <- function(vnam, df,
                    legtitle=NA,
                    mtitle="Box Map",
                    mult=1.5){
   var <- get.var(vnam,df)
   bb <- boxbreaks(var)
   tm_shape(df) +
      tm_fill(vnam,title=legtitle,
              breaks=bb,
              palette="Blues",
           labels = c("lower outlier",
                      "< 25%",
                      "25% - 50%",
                      "50% - 75%",
                      "> 75%",
                      "upper outlier"))  +
   tm_borders(lwd = 0.1,
             alpha = 0.3) +
   tm_layout(panel.show = TRUE,
            panel.labels = vnam,
            panel.label.size = 0.5,
            legend.show=FALSE)
}

As seen in the code chunks below, the boxmap function can be easily applied to create box maps for the cumulative confirmed cases rates and death rates without much duplicate code. The box maps are then arranged together using tmap_arrange() to aid easier spotting of patterns over space and time.

6.1.1 Cumulative Confirmed Cases Rates By Month

tmap_arrange(boxmap("POSITIF_Mar20", jakartaDKI_rates),
             boxmap("POSITIF_Apr20", jakartaDKI_rates),
             boxmap("POSITIF_May20", jakartaDKI_rates),
             boxmap("POSITIF_Jun20", jakartaDKI_rates),
             boxmap("POSITIF_Jul20", jakartaDKI_rates),
             boxmap("POSITIF_Aug20", jakartaDKI_rates),
             boxmap("POSITIF_Sep20", jakartaDKI_rates),
             boxmap("POSITIF_Oct20", jakartaDKI_rates),
             boxmap("POSITIF_Nov20", jakartaDKI_rates),
             boxmap("POSITIF_Dec20", jakartaDKI_rates),
             boxmap("POSITIF_Jan21", jakartaDKI_rates),
             boxmap("POSITIF_Feb21", jakartaDKI_rates),
             boxmap("POSITIF_Mar21", jakartaDKI_rates),
             boxmap("POSITIF_Apr21", jakartaDKI_rates),
             boxmap("POSITIF_May21", jakartaDKI_rates),
             boxmap("POSITIF_Jun21", jakartaDKI_rates),
             boxmap("POSITIF_Jul21", jakartaDKI_rates))

6.1.2 Cumulative Death Rates By Month

tmap_arrange(boxmap("MENINGGAL_Mar20", jakartaDKI_rates),
             boxmap("MENINGGAL_Apr20", jakartaDKI_rates),
             boxmap("MENINGGAL_May20", jakartaDKI_rates),
             boxmap("MENINGGAL_Jun20", jakartaDKI_rates),
             boxmap("MENINGGAL_Jul20", jakartaDKI_rates),
             boxmap("MENINGGAL_Aug20", jakartaDKI_rates),
             boxmap("MENINGGAL_Sep20", jakartaDKI_rates),
             boxmap("MENINGGAL_Oct20", jakartaDKI_rates),
             boxmap("MENINGGAL_Nov20", jakartaDKI_rates),
             boxmap("MENINGGAL_Dec20", jakartaDKI_rates),
             boxmap("MENINGGAL_Jan21", jakartaDKI_rates),
             boxmap("MENINGGAL_Feb21", jakartaDKI_rates),
             boxmap("MENINGGAL_Mar21", jakartaDKI_rates),
             boxmap("MENINGGAL_Apr21", jakartaDKI_rates),
             boxmap("MENINGGAL_May21", jakartaDKI_rates),
             boxmap("MENINGGAL_Jun21", jakartaDKI_rates),
             boxmap("MENINGGAL_Jul21", jakartaDKI_rates))


ggplot(data=jakartaDKI_rates, 
       aes(x = "", 
           y = MENINGGAL_Mar20)) +
  geom_boxplot()

6.2 Relative Risk Map

Relative risk map analysis helps to compare the observed mortality rate to a national standard across space.

rel_risk_map <- tm_shape(jakartaDKI_risk) +
  tm_fill("RELATIVE_RISK",
          n = 5,
          style = "jenks",
          palette = "Blues") +
  tm_borders(lwd=0.1, alpha = 1) +
  tm_layout(main.title = "Relative Risk",
              main.title.size = 1,
              main.title.position="center",
              legend.text.size = 1,
              legend.height = 0.25,
              legend.width = 0.35)

rel_risk_map